@JetBrains

Creator · JetBrains

Last updated · Aug 24, 2026

importing-a-codebase

REVIEW · 69Registry indexed

Use when the repo holds real source code but no specs: the existing-codebase branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for empty workspaces (starting-a-new-project) or feature work in a specced proj

OpenAgentSkill Trust Score
69/100

Sandbox only

Quality63/100
Audit79/100
Stars38
Verified installs0

Install targets

Codex install prompt

Install the "importing-a-codebase" agent skill from https://github.com/JetBrains/thinkrail/tree/main/packages/pi-thinkrail-workflow/skills/importing-a-codebase. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when the repo holds real source code but no specs: the existing-codebase branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for empty workspaces (starting-a-new-project) or feature work in a specced project (brainstorming). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jetbrains-importing-a-codebase","task":"Install importing-a-codebase","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

Research agents

I need my agent to research a topic, compare sources, and produce a concise report.

Agent fit

Claude Code + Cursor + CLI

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add JetBrains/thinkrail --skill importing-a-codebase

Maintenance

fresh

Pushed today

Risk

Needs review

Financial research output is not financial advice; require human review before any live investment decision

GitHub quality

38

63/100 Quality · 77/100 Trust

Coverage tags

ResearchResearch agentsagent-skill

Review notes

Financial research output is not financial advice; require human review before any live investment decision · Low GitHub adoption signal

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
63

Useful candidate, but compare it with alternatives before adopting.

Trust

Sandbox only
69

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
79

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

38 GitHub stars

Repo activity

38 stars, 8 forks

Maintenance

Pushed today

License

Apache-2.0

Install

npx skills add JetBrains/thinkrail --skill importing-a-codebase

Install safety

standard package or runtime install path

Permission surface

filesystem or document access, network or browser access

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 38 GitHub stars

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

View technical data+

Suited tasks

  • GitHub automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Inspect repository metadata

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add JetBrains/thinkrail --skill importing-a-codebase
Policy
review
Human review
yes

Trust and risk

Trust
69/100
Audit
79/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add JetBrains/thinkrail --skill importing-a-codebase

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Low GitHub adoption signal
  • No OpenAgentSkill engagement data yet
  • Financial research output is not financial advice; require human review before any live investment decision

Agent safety v2

59/100 · Review before install

Reviewed with permission notesreview

Usable candidate, but the agent should surface permission and audit notes before installation.

Require human approval before installing into a real workspace.

Resolve via API

medium

Browser automation

Skill may drive a browser or interact with web pages.

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

  • Financial research output is not financial advice; require human review before any live investment decision

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use importing-a-codebase in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20importing-a-codebase%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jetbrains-importing-a-codebase/install
Install command: npx skills add JetBrains/thinkrail --skill importing-a-codebase
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use importing-a-codebase for this task. Review https://www.openagentskill.com/api/skills/jetbrains-importing-a-codebase/install, then install with: npx skills add JetBrains/thinkrail --skill importing-a-codebase

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

62/100

GitHub automation

Platforms

Claude Code, Cursor

Audit report

Needs review · 79/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for GitHub automation

Prototype with this skill first; keep a fallback candidate ready.

62
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

GitHub automation

Trust label

Prototype first

Install path

Command ready

Use when

  • GitHub automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 63/100 quality profile

review first

  • Low GitHub adoption signal
  • No OpenAgentSkill engagement data yet

Implementation path

  1. 1Install it in a sandbox agent and run one GitHub automation task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

69
OpenAgentSkill Trust Score

GitHub adoption

CHECK

38 GitHub stars

Stars/forks activity

CHECK

38 stars, 8 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

Apache-2.0

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 38 GitHub stars
  • Stars/forks activity: 38 stars, 8 forks; issue activity unavailable in current metadata
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

63
GitHub stars
38
Freshness
Today
Install ready
Yes
License
Apache-2.0
Review before install: Low GitHub adoption signal

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: importing-a-codebase description: "Use when the repo holds real source code but no specs: the existing-codebase branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for empty workspaces (starting-a-new-project) or feature work in a specced project (brainstorming)." ---

# Importing a codebase

The workspace holds real code but no specs. **Reverse-engineer the spec graph the project should have had.** When the repo already carries real spec-like documents, build the graph around them, not parallel to them. Do as much as possible yourself, from the files; ask the user only where the code genuinely can't tell you and the answer changes a spec.

**Hold the writing-specs bar.** Read that concept skill before drafting — everything in this flow is inferred rather than confirmed, so its honesty rules (draft until the user reviews, unconfirmed marked inline) bind hardest here.

## 1. Read first, ask last

Survey before you ask a single question. Read, in roughly this order:

- **Agent files (mine these first — they state intent + conventions directly):** `AGENTS.md`, `CLAUDE.md`, `.cursor/rules/*`, `.cursorrules`, `.github/copilot-instructions.md`, `GEMINI.md`, `.windsurfrules`. - **Docs:** `README`, `docs/`, `CONTRIBUTING`, ADRs. - **Manifests & layout:** `package.json` / `pyproject.toml` / `go.mod` / `Cargo.toml`, workspace globs, `tree`-style structure, entry points, build/test scripts. - **Code:** entry points and the top of each candidate module — enough to see responsibilities and the dependency edges between them.

While you read, collect **adoption candidates**: durable, declarative documents that state the world as it is — architecture/design docs, ADRs / decision records, domain glossaries, protocol/contract docs. Never candidates (input only): READMEs, CONTRIBUTING, changelogs, roadmaps, TODOs, implementation plans (finished or planned), generated API docs.

Confirm with the spec tools (`spec_grep` / `spec_graph`) that there's no graph yet. If specs already exist, stop and hand back to the `setting-up-a-project` dispatcher — this flow is for un-specced repos.

## 2. Build a working model

From what you read, form a working model of what the project **is** and how it's **shaped** — held in the conversation, not written to a file (this flow declares no working files):

``` what: one-sentence purpose (the job the codebase does) domain: the space it's in stack: languages / frameworks / runtime modules: the real boundaries + the dependency edges between them (who imports whom) invariants: rules the code already enforces (layering, "X never imports Y", public surfaces) decisions: non-obvious choices visible in the code (and where the "why" is missing) ```

Agent files and READMEs usually hand you `what`, `invariants`, and `decisions` for free — prefer them over re-deriving from code.

## 3. Interview only the gaps

Ask **only** what the files can't answer and that would change a spec — typically: the primary job / who it's for, explicit non-goals, and the *why* behind a non-obvious decision. Batch them per the **asking-user-questions** concept skill; infer a concrete answer and let the user correct it rather than asking open-ended.

If adoption candidates exist, add one question to the same round: a multiSelect listing them (grouped when many — an `adr/` set is one option) — which should become spec-graph nodes? A contradiction between a candidate and the code found by now goes into the round too (confirm the correction). Skipped or declined → adopt none; candidates stay input, noted at hand-off.

If the files answered everything material, **skip the interview** and say so — don't manufacture questions (adoption candidates alone still make a round — the offer is never dropped as "no gaps"). A skipped/declined question is not a blocker: record the assumption inline in the spec, marked unconfirmed.

## 4. Draft the graph, top-down

Save with the spec tools as you go (`spec_create` per node, `edit` for prose). Order:

1. **`goal-and-requirements.md`** (`type: goal-and-requirements`) — the goal + scope. This is the graph root; the confirmed intent lives here. 2. **`architecture.md`** (`type: architecture-design`, `parent: <goal id>`) — topology, the module boundaries, the real dependency edges (a small DAG only if it carries real information), and the invariants the code enforces. 3. **One short `SPEC.md` per genuine module** (`type: module-design`, or `submodule-design` for a directory-level module inside a package; `parent:` its enclosing module or `architecture`). Each states its **responsibility** and its **boundary** (allowed deps / forbidden reaches).

**Adopted docs become nodes in place.** First, for each accepted candidate: read it carefully, then add spec frontmatter where the file lies (`id`, `type`, `title`, `status: draft`, `parent`; `depends-on`/`references` only where real) — content untouched. A slot an adopted doc fills is not drafted again: an adopted architecture doc *is* the `architecture-design` node, an adopted module design doc *is* that module's node, an ADR earns a node only while its decision is still in force. Build the rest of the graph around them, linked by id. One exception to "content untouched": where an adopted doc is unclear or has drifted from the code, correct that content as part of adoption and call the correction out (in the interview round when caught in time, at hand-off otherwise).

Wire `parent` to mirror the code hierarchy and `depends-on` only on edges the code actually shows. Keep each file **you draft** to the **writing-specs** bar — its granularity and say-it-once rules decide what counts as a module and where shared edges live. If a boundary is genuinely unclear, ask, or leave that spec `draft` with a one-line note — don't guess elaborately.

## 5. Validate & hand off

- Run `spec_validate`; fix dangling links, duplicate ids, parent cycles. - Tell the user the specs are drafted on this workspace's branch — **review them in Changes; nothing merges until they approve** — and summarize what you inferred vs. what they confirmed, which docs were adopted vs. left as input, and any drift corrections made. - Point at `brainstorming` for feature work from here on — **this workflow ends here**.

Technical details

Version
1.0.0
License
Apache-2.0
Last updated
Aug 24, 2026
Published
Aug 24, 2026

Decision snapshot

Fallback candidate

62
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

79
Needs review
Security
84/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for importing-a-codebase, ready for a manual X post.

Curator note
importing-a-codebase: Use when the repo holds real source code but no specs: the existing-codebase branch of settin...

38 stars

https://www.openagentskill.com/skills/jetbrains-importing-a-codebase?ref=x
Open X draft
Optional reply with install command
Listing + install path for importing-a-codebase:
https://www.openagentskill.com/skills/jetbrains-importing-a-codebase?ref=x

Install: npx skills add JetBrains/thinkrail --skill importing-a-codebase

Listing source

Registry indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
JetBrains
Indexed by
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Owner claim

Claim this skill listing

This Registry indexed listing is attributed to JetBrains but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

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Author

J

JetBrains

@jetbrains

Platform fit

Health signals

GitHub stars
38
Quality score
35/100
Last GitHub push
Aug 24, 2026
Framework hints
Unknown
OpenAgentSkill views
0
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

69
  • GitHub adoption38 GitHub starsCHECK
  • Stars/forks activity38 stars, 8 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenancePushed todayPASS
  • License clarityApache-2.0PASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime risknetwork or browser surfacePASS